R Notebook

is.na() applied to non-(list or vector) of type 'expression'is.na() applied to non-(list or vector) of type 'expression'

The "ward" method has been renamed to "ward.D"; note new "ward.D2"

Bootstrap (r = 0.5)... Done.

inappropriate distance matrices are omitted in computation: r =  0.5

Bootstrap (r = 0.75)... Done.

inappropriate distance matrices are omitted in computation: r =  0.75

Bootstrap (r = 1.0)... Done.

inappropriate distance matrices are omitted in computation: r =  1

Bootstrap (r = 1.25)... Done.

inappropriate distance matrices are omitted in computation: r =  1.25

'dendrogram' with 2 branches and 4 members total, at height 2.227811 

Joining, by = "y"

Coordinate system already present. Adding new coordinate system, which will replace the existing one.

Joining, by = c("Strain", "Cell", "Expt", "MOI")
Column `Strain` joining factors with different levels, coercing to character vectorJoining, by = "Virus_Cell"

The "ward" method has been renamed to "ward.D"; note new "ward.D2"

Bootstrap (r = 0.44)... Done.

inappropriate distance matrices are omitted in computation: r =  0.444444444444444

Bootstrap (r = 0.56)... Done.
Bootstrap (r = 0.67)... Done.
Bootstrap (r = 0.78)... Done.
Bootstrap (r = 0.89)... Done.
Bootstrap (r = 1.0)... Done.
Bootstrap (r = 1.11)... Done.
Bootstrap (r = 1.22)... Done.
Bootstrap (r = 1.33)... Done.

Joining, by = "y"
Joining, by = "label"
Column `label` joining factors with different levels, coercing to character vectorJoining, by = "label"
Column `label` joining character vector and factor, coercing into character vectorJoining, by = "x"
Coordinate system already present. Adding new coordinate system, which will replace the existing one.

Joining, by = "Virus_Cell"

`geom_smooth()` using method = 'loess' and formula 'y ~ x'

Searching for genomes in: /Users/jacobn07/Documents/GFHK99_Multiplicity/Data/CellRanger_Output/outs/filtered_gene_bc_matrices_mex 
Using WT_DF1_0.07 in folder: /Users/jacobn07/Documents/GFHK99_Multiplicity/Data/CellRanger_Output/outs/filtered_gene_bc_matrices_mex/WT_DF1_0.07 
Loaded matrix information
Loaded gene information
Loaded barcode information
Could not find summary csv: 
     /Users/jacobn07/Documents/GFHK99_Multiplicity/Data/CellRanger_Output/outs/metrics_summary.csv.
This file is only necessary if you are performing depth-normalization (calling the equalize_gbms function) in R.
If this pipestance was produced by `cellranger aggr` with the default parameters, depth-normalization in R (via equalize_gbms) is not necessary.
Searching for genomes in: /Users/jacobn07/Documents/GFHK99_Multiplicity/Data/CellRanger_Output/outs/filtered_gene_bc_matrices_mex 
Using WT_DF1_0.2 in folder: /Users/jacobn07/Documents/GFHK99_Multiplicity/Data/CellRanger_Output/outs/filtered_gene_bc_matrices_mex/WT_DF1_0.2 
Loaded matrix information
Loaded gene information
Loaded barcode information
Could not find summary csv: 
     /Users/jacobn07/Documents/GFHK99_Multiplicity/Data/CellRanger_Output/outs/metrics_summary.csv.
This file is only necessary if you are performing depth-normalization (calling the equalize_gbms function) in R.
If this pipestance was produced by `cellranger aggr` with the default parameters, depth-normalization in R (via equalize_gbms) is not necessary.
Searching for genomes in: /Users/jacobn07/Documents/GFHK99_Multiplicity/Data/CellRanger_Output/outs/filtered_gene_bc_matrices_mex 
Using WT_DF1_0.6 in folder: /Users/jacobn07/Documents/GFHK99_Multiplicity/Data/CellRanger_Output/outs/filtered_gene_bc_matrices_mex/WT_DF1_0.6 
Loaded matrix information
Loaded gene information
Loaded barcode information
Could not find summary csv: 
     /Users/jacobn07/Documents/GFHK99_Multiplicity/Data/CellRanger_Output/outs/metrics_summary.csv.
This file is only necessary if you are performing depth-normalization (calling the equalize_gbms function) in R.
If this pipestance was produced by `cellranger aggr` with the default parameters, depth-normalization in R (via equalize_gbms) is not necessary.
Searching for genomes in: /Users/jacobn07/Documents/GFHK99_Multiplicity/Data/CellRanger_Output/outs/filtered_gene_bc_matrices_mex 
Using WT_DF1_1.8 in folder: /Users/jacobn07/Documents/GFHK99_Multiplicity/Data/CellRanger_Output/outs/filtered_gene_bc_matrices_mex/WT_DF1_1.8 
Loaded matrix information
Loaded gene information
Loaded barcode information
Could not find summary csv: 
     /Users/jacobn07/Documents/GFHK99_Multiplicity/Data/CellRanger_Output/outs/metrics_summary.csv.
This file is only necessary if you are performing depth-normalization (calling the equalize_gbms function) in R.
If this pipestance was produced by `cellranger aggr` with the default parameters, depth-normalization in R (via equalize_gbms) is not necessary.
Searching for genomes in: /Users/jacobn07/Documents/GFHK99_Multiplicity/Data/CellRanger_Output/outs/filtered_gene_bc_matrices_mex 
Using WT_MDCK_0.07 in folder: /Users/jacobn07/Documents/GFHK99_Multiplicity/Data/CellRanger_Output/outs/filtered_gene_bc_matrices_mex/WT_MDCK_0.07 
Loaded matrix information
Loaded gene information
Loaded barcode information
Could not find summary csv: 
     /Users/jacobn07/Documents/GFHK99_Multiplicity/Data/CellRanger_Output/outs/metrics_summary.csv.
This file is only necessary if you are performing depth-normalization (calling the equalize_gbms function) in R.
If this pipestance was produced by `cellranger aggr` with the default parameters, depth-normalization in R (via equalize_gbms) is not necessary.
Searching for genomes in: /Users/jacobn07/Documents/GFHK99_Multiplicity/Data/CellRanger_Output/outs/filtered_gene_bc_matrices_mex 
Using WT_MDCK_0.2 in folder: /Users/jacobn07/Documents/GFHK99_Multiplicity/Data/CellRanger_Output/outs/filtered_gene_bc_matrices_mex/WT_MDCK_0.2 
Loaded matrix information
Loaded gene information
Loaded barcode information
Could not find summary csv: 
     /Users/jacobn07/Documents/GFHK99_Multiplicity/Data/CellRanger_Output/outs/metrics_summary.csv.
This file is only necessary if you are performing depth-normalization (calling the equalize_gbms function) in R.
If this pipestance was produced by `cellranger aggr` with the default parameters, depth-normalization in R (via equalize_gbms) is not necessary.
Searching for genomes in: /Users/jacobn07/Documents/GFHK99_Multiplicity/Data/CellRanger_Output/outs/filtered_gene_bc_matrices_mex 
Using WT_MDCK_0.6 in folder: /Users/jacobn07/Documents/GFHK99_Multiplicity/Data/CellRanger_Output/outs/filtered_gene_bc_matrices_mex/WT_MDCK_0.6 
Loaded matrix information
Loaded gene information
Loaded barcode information
Could not find summary csv: 
     /Users/jacobn07/Documents/GFHK99_Multiplicity/Data/CellRanger_Output/outs/metrics_summary.csv.
This file is only necessary if you are performing depth-normalization (calling the equalize_gbms function) in R.
If this pipestance was produced by `cellranger aggr` with the default parameters, depth-normalization in R (via equalize_gbms) is not necessary.
Searching for genomes in: /Users/jacobn07/Documents/GFHK99_Multiplicity/Data/CellRanger_Output/outs/filtered_gene_bc_matrices_mex 
Using WT_MDCK_1.8 in folder: /Users/jacobn07/Documents/GFHK99_Multiplicity/Data/CellRanger_Output/outs/filtered_gene_bc_matrices_mex/WT_MDCK_1.8 
Loaded matrix information
Loaded gene information
Loaded barcode information
Could not find summary csv: 
     /Users/jacobn07/Documents/GFHK99_Multiplicity/Data/CellRanger_Output/outs/metrics_summary.csv.
This file is only necessary if you are performing depth-normalization (calling the equalize_gbms function) in R.
If this pipestance was produced by `cellranger aggr` with the default parameters, depth-normalization in R (via equalize_gbms) is not necessary.

Joining, by = c("Cell", "MOI")

Searching for genomes in: /Users/jacobn07/Documents/GFHK99_Multiplicity/Data/CellRanger_Output/outs/filtered_gene_bc_matrices_mex 
Using DF1_0.02 in folder: /Users/jacobn07/Documents/GFHK99_Multiplicity/Data/CellRanger_Output/outs/filtered_gene_bc_matrices_mex/DF1_0.02 
Loaded matrix information
Loaded gene information
Loaded barcode information
Could not find summary csv: 
     /Users/jacobn07/Documents/GFHK99_Multiplicity/Data/CellRanger_Output/outs/metrics_summary.csv.
This file is only necessary if you are performing depth-normalization (calling the equalize_gbms function) in R.
If this pipestance was produced by `cellranger aggr` with the default parameters, depth-normalization in R (via equalize_gbms) is not necessary.
Searching for genomes in: /Users/jacobn07/Documents/GFHK99_Multiplicity/Data/CellRanger_Output/outs/filtered_gene_bc_matrices_mex 
Using DF1_0.07 in folder: /Users/jacobn07/Documents/GFHK99_Multiplicity/Data/CellRanger_Output/outs/filtered_gene_bc_matrices_mex/DF1_0.07 
Loaded matrix information
Loaded gene information
Loaded barcode information
Could not find summary csv: 
     /Users/jacobn07/Documents/GFHK99_Multiplicity/Data/CellRanger_Output/outs/metrics_summary.csv.
This file is only necessary if you are performing depth-normalization (calling the equalize_gbms function) in R.
If this pipestance was produced by `cellranger aggr` with the default parameters, depth-normalization in R (via equalize_gbms) is not necessary.
Searching for genomes in: /Users/jacobn07/Documents/GFHK99_Multiplicity/Data/CellRanger_Output/outs/filtered_gene_bc_matrices_mex 
Using DF1_0.2 in folder: /Users/jacobn07/Documents/GFHK99_Multiplicity/Data/CellRanger_Output/outs/filtered_gene_bc_matrices_mex/DF1_0.2 
Loaded matrix information
Loaded gene information
Loaded barcode information
Could not find summary csv: 
     /Users/jacobn07/Documents/GFHK99_Multiplicity/Data/CellRanger_Output/outs/metrics_summary.csv.
This file is only necessary if you are performing depth-normalization (calling the equalize_gbms function) in R.
If this pipestance was produced by `cellranger aggr` with the default parameters, depth-normalization in R (via equalize_gbms) is not necessary.
Searching for genomes in: /Users/jacobn07/Documents/GFHK99_Multiplicity/Data/CellRanger_Output/outs/filtered_gene_bc_matrices_mex 
Using DF1_0.6 in folder: /Users/jacobn07/Documents/GFHK99_Multiplicity/Data/CellRanger_Output/outs/filtered_gene_bc_matrices_mex/DF1_0.6 
Loaded matrix information
Loaded gene information
Loaded barcode information
Could not find summary csv: 
     /Users/jacobn07/Documents/GFHK99_Multiplicity/Data/CellRanger_Output/outs/metrics_summary.csv.
This file is only necessary if you are performing depth-normalization (calling the equalize_gbms function) in R.
If this pipestance was produced by `cellranger aggr` with the default parameters, depth-normalization in R (via equalize_gbms) is not necessary.
Searching for genomes in: /Users/jacobn07/Documents/GFHK99_Multiplicity/Data/CellRanger_Output/outs/filtered_gene_bc_matrices_mex 
Using MDCK_0.02 in folder: /Users/jacobn07/Documents/GFHK99_Multiplicity/Data/CellRanger_Output/outs/filtered_gene_bc_matrices_mex/MDCK_0.02 
Loaded matrix information
Loaded gene information
Loaded barcode information
Could not find summary csv: 
     /Users/jacobn07/Documents/GFHK99_Multiplicity/Data/CellRanger_Output/outs/metrics_summary.csv.
This file is only necessary if you are performing depth-normalization (calling the equalize_gbms function) in R.
If this pipestance was produced by `cellranger aggr` with the default parameters, depth-normalization in R (via equalize_gbms) is not necessary.
Searching for genomes in: /Users/jacobn07/Documents/GFHK99_Multiplicity/Data/CellRanger_Output/outs/filtered_gene_bc_matrices_mex 
Using MDCK_0.07 in folder: /Users/jacobn07/Documents/GFHK99_Multiplicity/Data/CellRanger_Output/outs/filtered_gene_bc_matrices_mex/MDCK_0.07 
Loaded matrix information
Loaded gene information
Loaded barcode information
Could not find summary csv: 
     /Users/jacobn07/Documents/GFHK99_Multiplicity/Data/CellRanger_Output/outs/metrics_summary.csv.
This file is only necessary if you are performing depth-normalization (calling the equalize_gbms function) in R.
If this pipestance was produced by `cellranger aggr` with the default parameters, depth-normalization in R (via equalize_gbms) is not necessary.
Searching for genomes in: /Users/jacobn07/Documents/GFHK99_Multiplicity/Data/CellRanger_Output/outs/filtered_gene_bc_matrices_mex 
Using MDCK_0.2 in folder: /Users/jacobn07/Documents/GFHK99_Multiplicity/Data/CellRanger_Output/outs/filtered_gene_bc_matrices_mex/MDCK_0.2 
Loaded matrix information
Loaded gene information
Loaded barcode information
Could not find summary csv: 
     /Users/jacobn07/Documents/GFHK99_Multiplicity/Data/CellRanger_Output/outs/metrics_summary.csv.
This file is only necessary if you are performing depth-normalization (calling the equalize_gbms function) in R.
If this pipestance was produced by `cellranger aggr` with the default parameters, depth-normalization in R (via equalize_gbms) is not necessary.
Searching for genomes in: /Users/jacobn07/Documents/GFHK99_Multiplicity/Data/CellRanger_Output/outs/filtered_gene_bc_matrices_mex 
Using MDCK_0.6 in folder: /Users/jacobn07/Documents/GFHK99_Multiplicity/Data/CellRanger_Output/outs/filtered_gene_bc_matrices_mex/MDCK_0.6 
Loaded matrix information
Loaded gene information
Loaded barcode information
Could not find summary csv: 
     /Users/jacobn07/Documents/GFHK99_Multiplicity/Data/CellRanger_Output/outs/metrics_summary.csv.
This file is only necessary if you are performing depth-normalization (calling the equalize_gbms function) in R.
If this pipestance was produced by `cellranger aggr` with the default parameters, depth-normalization in R (via equalize_gbms) is not necessary.

Joining, by = c("Cell", "MOI")
Joining, by = c("Cell", "MOI")

Joining, by = c("Cell", "MOI")

Joining, by = c("Cell", "MOI")
Joining, by = c("Cell", "MOI")

[1] 1873

Joining, by = c("Cell", "MOI")

Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: WT ~ P3_NP + (1 | MOI)
   Data: Norm.df1 %>% filter(Cell == "DF1")

REML criterion at convergence: 1803.7

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.4141 -0.7288 -0.2562  0.7354  4.4126 

Random effects:
 Groups   Name        Variance Std.Dev.
 MOI      (Intercept) 0.3912   0.6255  
 Residual             0.2486   0.4986  
Number of obs: 1228, groups:  MOI, 4

Fixed effects:
              Estimate Std. Error         df t value            Pr(>|t|)    
(Intercept)    1.17468    0.31318    3.00373   3.751               0.033 *  
P3_NP          0.60818    0.03441 1223.67135  17.677 <0.0000000000000002 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
      (Intr)
P3_NP -0.026
[1] 4.073803
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: WT ~ P3_NP + (1 | MOI)
   Data: Norm.df1 %>% filter(Cell == "MDCK")

REML criterion at convergence: 593.3

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.2200 -0.7376 -0.1163  0.6324  3.6811 

Random effects:
 Groups   Name        Variance Std.Dev.
 MOI      (Intercept) 0.5677   0.7534  
 Residual             0.1406   0.3750  
Number of obs: 645, groups:  MOI, 4

Fixed effects:
             Estimate Std. Error        df t value             Pr(>|t|)    
(Intercept)   1.00385    0.37713   2.99782   2.662               0.0763 .  
P3_NP         0.38550    0.04631 641.02927   8.324 0.000000000000000513 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
      (Intr)
P3_NP -0.023
[1] 2.454709
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: WT ~ P3_NP * Cell + (1 | MOI)
   Data: Norm.df1

REML criterion at convergence: 2532.2

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.4888 -0.7033 -0.2233  0.6479  4.5748 

Random effects:
 Groups   Name        Variance Std.Dev.
 MOI      (Intercept) 0.4129   0.6426  
 Residual             0.2217   0.4709  
Number of obs: 1873, groups:  MOI, 4

Fixed effects:
                 Estimate Std. Error         df t value            Pr(>|t|)    
(Intercept)       1.17772    0.32169    3.00631   3.661              0.0351 *  
P3_NP             0.58077    0.03193 1866.51899  18.188 <0.0000000000000002 ***
CellMDCK         -0.22722    0.02639 1866.11271  -8.611 <0.0000000000000002 ***
P3_NP:CellMDCK    0.01489    0.05568 1866.07896   0.267              0.7892    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) P3_NP  ClMDCK
P3_NP       -0.024              
CellMDCK    -0.030  0.269       
P3_NP:CMDCK  0.014 -0.474 -0.479
[1] 3.801894
[1] 0.4376587
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: WT ~ Cell + (1 | MOI)
   Data: Norm.df1

REML criterion at convergence: 2912.8

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.1172 -0.6845 -0.1179  0.3882  4.2010 

Random effects:
 Groups   Name        Variance Std.Dev.
 MOI      (Intercept) 0.5762   0.7591  
 Residual             0.2727   0.5222  
Number of obs: 1873, groups:  MOI, 4

Fixed effects:
              Estimate Std. Error         df t value            Pr(>|t|)    
(Intercept)    1.31219    0.37986    3.00366   3.454              0.0407 *  
CellMDCK      -0.24956    0.02565 1868.08101  -9.729 <0.0000000000000002 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
         (Intr)
CellMDCK -0.024
[1] 0.4376587
[1] 0.7782794

Joining, by = c("Cell", "MOI")

Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: value ~ variable + (1 | MOI)
   Data: Melt.df2

REML criterion at convergence: 3050.7

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.3174 -0.7854  0.0074  0.7636  2.5500 

Random effects:
 Groups   Name        Variance Std.Dev.
 MOI      (Intercept) 0.2448   0.4948  
 Residual             0.2961   0.5441  
Number of obs: 1865, groups:  MOI, 4

Fixed effects:
              Estimate Std. Error         df t value Pr(>|t|)  
(Intercept)    1.18457    0.24813    3.01658   4.774   0.0172 *
variableVAR    0.04726    0.02522 1860.01080   1.874   0.0611 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr)
variableVAR -0.053
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: Test ~ Cell * Help + (1 | MOI)
   Data: Sum.df

REML criterion at convergence: 6353.6

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.1149 -0.6928 -0.0438  0.5786  3.9675 

Random effects:
 Groups   Name        Variance Std.Dev.
 MOI      (Intercept) 0.6928   0.8323  
 Residual             0.3017   0.5492  
Number of obs: 3845, groups:  MOI, 5

Fixed effects:
                        Estimate Std. Error         df t value             Pr(>|t|)    
(Intercept)              1.09332    0.37266    4.00891   2.934               0.0425 *  
CellMDCK                -0.25449    0.02689 3837.07352  -9.464 < 0.0000000000000002 ***
Helpw/ mVAR2             0.39627    0.02676 3838.28675  14.810 < 0.0000000000000002 ***
CellMDCK:Helpw/ mVAR2    0.15808    0.03673 3837.03828   4.304            0.0000172 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) ClMDCK H/mVAR
CellMDCK    -0.026              
Helpw/mVAR2 -0.032  0.378       
CMDCK:H/mVA  0.018 -0.729 -0.632
[1] 0.4376587
[1] 0.2056718
[1] 1.490405
[1] 2.583851
[1] 2.818383
[1] 3.630781
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: Test ~ Cell * Help + (1 | MOI)
   Data: Sum.df %>% filter(MOI < 0.6)

REML criterion at convergence: 4069.7

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.5909 -0.7503 -0.1978  0.6255  3.4201 

Random effects:
 Groups   Name        Variance Std.Dev.
 MOI      (Intercept) 0.1748   0.4180  
 Residual             0.3773   0.6142  
Number of obs: 2169, groups:  MOI, 3

Fixed effects:
                        Estimate Std. Error         df t value             Pr(>|t|)    
(Intercept)              0.64536    0.24313    2.03858   2.654                0.115    
CellMDCK                -0.41472    0.04397 2163.01633  -9.432 < 0.0000000000000002 ***
Helpw/ mVAR2             0.17251    0.03642 2163.88745   4.737           0.00000231 ***
CellMDCK:Helpw/ mVAR2    0.54853    0.05587 2163.14850   9.818 < 0.0000000000000002 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) ClMDCK H/mVAR
CellMDCK    -0.061              
Helpw/mVAR2 -0.085  0.408       
CMDCK:H/mVA  0.044 -0.788 -0.617

         Rep     WT.Cells Helper.Cells 
          10          102          253 

Call:
lm(formula = Pp ~ Rep, data = Exp.Pp.1 %>% mutate(Rep = Rep %>% 
    factor(levels = c(1, 2, 3, 4, 5))))

Residuals:
     Min       1Q   Median       3Q      Max 
-0.25875 -0.11031  0.01063  0.08812  0.22500 

Coefficients:
            Estimate Std. Error t value             Pr(>|t|)    
(Intercept)  0.71500    0.04533  15.775 < 0.0000000000000002 ***
Rep2        -0.05625    0.06410  -0.878              0.38618    
Rep3        -0.05875    0.06410  -0.917              0.36565    
Rep4        -0.12250    0.06410  -1.911              0.06421 .  
Rep5        -0.24000    0.06410  -3.744              0.00065 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.1282 on 35 degrees of freedom
Multiple R-squared:  0.3186,    Adjusted R-squared:  0.2407 
F-statistic: 4.091 on 4 and 35 DF,  p-value: 0.007987


Call:
lm(formula = Pp ~ Rep, data = Exp.Pp.1 %>% mutate(Rep = Rep %>% 
    factor(levels = c(1, 2, 3, 4))))

Residuals:
     Min       1Q   Median       3Q      Max 
-0.25875 -0.10875  0.01063  0.06906  0.20375 

Coefficients:
            Estimate Std. Error t value             Pr(>|t|)    
(Intercept)  0.71500    0.04253  16.811 0.000000000000000366 ***
Rep2        -0.05625    0.06015  -0.935               0.3577    
Rep3        -0.05875    0.06015  -0.977               0.3371    
Rep4        -0.12250    0.06015  -2.037               0.0513 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.1203 on 28 degrees of freedom
Multiple R-squared:  0.1293,    Adjusted R-squared:  0.03598 
F-statistic: 1.386 on 3 and 28 DF,  p-value: 0.2676

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

is.na() applied to non-(list or vector) of type 'expression'Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.